Deep learning frameworks prioritize developer productivity through flexible, dynamic abstractions. Hardware accelerators, however, demand static, highly optimized instruction streams to perform at peak efficiency. A deep learning compiler bridges this gap by translating high-level mathematical descriptions into executable machine code. This translation process involves distinct stages that progressively lower the level of abstraction, moving from logical operators to hardware intrinsics.

Unlike general-purpose compilers that focus on scalar logic and complex control flow, an AI compiler targets massive parallelism and tensor algebra. The primary unit of optimization shifts from the basic block to the computational graph. This structural difference dictates how the compiler represents, analyzes, and transforms the code. For instance, a simple matrix multiplication represented mathematically as $C = A \times B$ involves high-level semantics regarding shape and data type that must be preserved during the initial phases of compilation.

This chapter establishes the architectural foundation of the compilation pipeline. We begin by defining the specific requirements of tensor processing units compared to standard CPUs. You will analyze the role of the Directed Acyclic Graph (DAG) as the central data structure for model execution and examine how Static Single Assignment (SSA) form facilitates dependency tracking in neural networks.

We will also distinguish between the two primary categories of Intermediate Representations (IR). You will identify the characteristics of graph-level IRs, which handle logical simplifications, and instruction-level IRs, which handle memory allocation and loop scheduling. The chapter concludes with a practical session where you will inspect the structure of TVM's Relay IR to observe these concepts in a production-grade system.